code: def generate_causal_lm_model_text(df):
      from llm_studio.python_configs.text_causal_language_modeling_config import (
          ConfigNLPCausalLMDataset,
      )
      from llm_studio.python_configs.text_causal_language_modeling_config import (
          ConfigProblemBase as ConfigCausalLMProblemBase,
      )
      from llm_studio.src.datasets.text_causal_language_modeling_ds import (
          CustomDataset as CausalLMCustomDataset,
      )
      from llm_studio.src.models.text_causal_language_modeling_model import (
          Model as CausalLMModel,
      )
  
      device = torch.device(
          "cuda" if torch.cuda.is_available() else "cpu"
      )
  
      cfg = ConfigCausalLMProblemBase(
          llm_backbone="MaxJeblick/llama2-0b-unit-test",
          dataset=ConfigNLPCausalLMDataset(
              system_column="system",
              prompt_column=("prompt",),
              answer_column="answer_column",
          ),
          tokenizer=ConfigNLPCausalLMTokenizer(
              max_length_prompt=256,
              max_length_answer=256,
              max_length=512,
          ),
      )
      cfg.architecture.backbone_dtype = "float32"
  
      dataset = CausalLMCustomDataset(df, cfg, mode="train")
      model = CausalLMModel(cfg).to(device).eval()
      dataloader = torch.utils.data.DataLoader(
          dataset, batch_size=16, shuffle=True
      )
  
      batch = next(iter(dataloader))
      batch = {k: v.to(device) for k, v in batch.items()}
      batch_padding(
          cfg,
          batch,
          mask_key="prompt_attention_mask",
          pad_keys=[
              "prompt_input_ids",
              "prompt_attention_mask",
              "prompt_special_tokens_mask",
          ],
      )
      with torch.no_grad():
          generated_text = dataset.tokenizer.decode(
              model.generate(batch, cfg)[0]
          )
  
      return generated_text
  
file_path: h2o-llmstudio/tests/src/models/test_dpo_modeling_model.py
imports: import pandas
  import pytest
  import torch
  from llm_studio.python_configs.text_causal_language_modeling_config import ConfigNLPCausalLMTokenizer
  from llm_studio.python_configs.text_dpo_modeling_config import ConfigDPODataset, ConfigProblemBase
  from llm_studio.src.datasets.text_dpo_modeling_ds import CustomDataset
  from llm_studio.src.models.text_dpo_modeling_model import Model
  from llm_studio.src.utils.data_utils import batch_padding
label_desc: Use image node_id 2 for calling a global function with example usage: generate_causal_lm_model_text(df) and returns: generated_text